6,424 research outputs found
Towards Social Autonomous Vehicles: Efficient Collision Avoidance Scheme Using Richardson's Arms Race Model
Background Road collisions and casualties pose a serious threat to commuters
around the globe. Autonomous Vehicles (AVs) aim to make the use of technology
to reduce the road accidents. However, the most of research work in the context
of collision avoidance has been performed to address, separately, the rear end,
front end and lateral collisions in less congested and with high
inter-vehicular distances. Purpose The goal of this paper is to introduce the
concept of a social agent, which interact with other AVs in social manners like
humans are social having the capability of predicting intentions, i.e.
mentalizing and copying the actions of each other, i.e. mirroring. The proposed
social agent is based on a human-brain inspired mentalizing and mirroring
capabilities and has been modelled for collision detection and avoidance under
congested urban road traffic.
Method We designed our social agent having the capabilities of mentalizing
and mirroring and for this purpose we utilized Exploratory Agent Based Modeling
(EABM) level of Cognitive Agent Based Computing (CABC) framework proposed by
Niazi and Hussain.
Results Our simulation and practical experiments reveal that by embedding
Richardson's arms race model within AVs, collisions can be avoided while
travelling on congested urban roads in a flock like topologies. The performance
of the proposed social agent has been compared at two different levels.Comment: 48 pages, 21 figure
Customer purchase behavior prediction in E-commerce: a conceptual framework and research agenda
Digital retailers are experiencing an increasing number of transactions coming from their consumers online, a consequence of the convenience in buying goods via E-commerce platforms. Such interactions compose complex behavioral patterns which can be analyzed through predictive analytics to enable businesses to understand consumer needs. In this abundance of big data and possible tools to analyze them, a systematic review of the literature is missing. Therefore, this paper presents a systematic literature review of recent research dealing with customer purchase prediction in the E-commerce context. The main contributions are a novel analytical framework and a research agenda in the field. The framework reveals three main tasks in this review, namely, the prediction of customer intents, buying sessions, and purchase decisions. Those are followed by their employed predictive methodologies and are analyzed from three perspectives. Finally, the research agenda provides major existing issues for further research in the field of purchase behavior prediction online
Behavior of early warnings near the critical temperature in the two-dimensional Ising model
Among the properties that are common to complex systems, the presence of
critical thresholds in the dynamics of the system is one of the most important.
Recently, there has been interest in the universalities that occur in the
behavior of systems near critical points. These universal properties make it
possible to estimate how far a system is from a critical threshold. Several
early-warning signals have been reported in time series representing systems
near catastrophic shifts. The proper understanding of these early-warnings may
allow the prediction and perhaps control of these dramatic shifts in a wide
variety of systems. In this paper we analyze this universal behavior for a
system that is a paradigm of phase transitions, the Ising model. We study the
behavior of the early-warning signals and the way the temporal correlations of
the system increase when the system is near the critical point.Comment: 20 pages, 8 figures, Submitted to PLOS ONE on Oct. 20th 2014.
PONE-D-14-4718
Ensemble Committees for Stock Return Classification and Prediction
This paper considers a portfolio trading strategy formulated by algorithms in
the field of machine learning. The profitability of the strategy is measured by
the algorithm's capability to consistently and accurately identify stock
indices with positive or negative returns, and to generate a preferred
portfolio allocation on the basis of a learned model. Stocks are characterized
by time series data sets consisting of technical variables that reflect market
conditions in a previous time interval, which are utilized produce binary
classification decisions in subsequent intervals. The learned model is
constructed as a committee of random forest classifiers, a non-linear support
vector machine classifier, a relevance vector machine classifier, and a
constituent ensemble of k-nearest neighbors classifiers. The Global Industry
Classification Standard (GICS) is used to explore the ensemble model's efficacy
within the context of various fields of investment including Energy, Materials,
Financials, and Information Technology. Data from 2006 to 2012, inclusive, are
considered, which are chosen for providing a range of market circumstances for
evaluating the model. The model is observed to achieve an accuracy of
approximately 70% when predicting stock price returns three months in advance.Comment: 15 pages, 4 figures, Neukom Institute Computational Undergraduate
Research prize - second plac
Measuring information-transfer delays
In complex networks such as gene networks, traffic systems or brain circuits it is important to understand how long it takes for the different parts of the network to effectively influence one another. In the brain, for example, axonal delays between brain areas can amount to several tens of milliseconds, adding an intrinsic component to any timing-based processing of information. Inferring neural interaction delays is thus needed to interpret the information transfer revealed by any analysis of directed interactions across brain structures. However, a robust estimation of interaction delays from neural activity faces several challenges if modeling assumptions on interaction mechanisms are wrong or cannot be made. Here, we propose a robust estimator for neuronal interaction delays rooted in an information-theoretic framework, which allows a model-free exploration of interactions. In particular, we extend transfer entropy to account for delayed source-target interactions, while crucially retaining the conditioning on the embedded target state at the immediately previous time step. We prove that this particular extension is indeed guaranteed to identify interaction delays between two coupled systems and is the only relevant option in keeping with Wiener’s principle of causality. We demonstrate the performance of our approach in detecting interaction delays on finite data by numerical simulations of stochastic and deterministic processes, as well as on local field potential recordings. We also show the ability of the extended transfer entropy to detect the presence of multiple delays, as well as feedback loops. While evaluated on neuroscience data, we expect the estimator to be useful in other fields dealing with network dynamics
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